The advent of Multi-Access Edge Computing (MEC) has enabled service providers to mitigate high network latencies often encountered in accessing cloud services. The key idea of MEC involves service providers deploying containerized application services on MEC servers situated near Internet-of-Things (IoT) device users. The users access these services via wireless base stations with ultra low latency. Computation tasks of IoT devices can then either be executed locally on the devices or on the MEC servers. A key cornerstone of the MEC environment is an offloading policy utilized to determine whether to execute computation tasks on IoT devices or to offload the tasks to MEC servers for processing. In this work, we propose a two phase Probabilistic Model Checking based offloading policy catering to IoT device user preferences. The first stage evaluates the trade-offs between local vs server execution while the second stage evaluates the trade-offs between choice of wireless communication bands for offloaded tasks. We present experimental results in practical scenarios on data gathered from an IoT test-bed setup with benchmark applications to show the benefits of an adaptive preference-aware approach over conventional approaches in the MEC offloading context.

Multi-Access Edge Computing (MEC) is increasingly being adopted as the de facto enabler for ultra-low latency access to application services. By placing application services on MEC servers situated in proximity to end users, MEC avoids the large network latencies frequently experienced while accessing cloud services. MEC is envisioned as the fundamental enabler for a number of ultra-low latency safety-critical systems, including data inferencing for autonomous vehicles amongst others. The MEC paradigm is, however, highly susceptible to various types of faults such as MEC server downtime, communication link faults, network hardware faults and so on owing to the heterogeneity of hardware configurations and diverse geographies of operations. For real-time and safety-critical workloads, averting the impact of faults is a key facet. To address this challenge, we synthesize a fault classification policy for MEC that categorizes a fault as critical requiring immediate rectification or non-critical by leveraging Probabilistic Model Checking, a Formal Methods technique, to ensure probabilistic guarantees with respect to a specified failure context. We present experimental results on a real-world datasets to show the effectiveness of our approach.

With autonomous automotives routinely leveraging computationally intensive tasks to enable robust navigation, a number of design challenges have emerged. Whilst processing tasks is traditionally carried out on the vehicles, the emergence of Multi-Access Edge Computing (MEC) has paved the way to transfer such tasks to be executed not just locally on the vehicles but also offloads such tasks to powerful MEC servers co-located with cellular base-stations. In a vehicular MEC environment, a computation offloading policy determines which tasks are to be executed locally on the vehicles and which tasks are to be transferred to MEC servers for further processing. In recent years, a number of offloading policies in have been delineated considering several optimization objectives. However, the uncertainty associated with observability metrics due to the high stochasticity of the network environment has been less explored. In this paper, we highlight the impact of this uncertainty on timeliness guarantees for safe autonomy and propose a quantitative model checking approach towards offloading tasks to MEC servers. We believe our article can motivate new research directions towards offloading in the vehicular MEC context.

In Multi-Access Edge Computing (MEC), a number of mechanisms exist to determine the optimal placement of monolithic service workflows. For applications designed as microservice workflow architectures, service placement schemes need to be revisited owing to the inherent interdependencies which exist between microservices. The dynamic environment, with stochastic user movement and service invocations, along with a large placement configuration space makes microservice placement in MEC a challenging task. Additionally, owing to user mobility, a placement scheme may need to be recalibrated, triggering service migrations to maintain the advantages offered by MEC. Existing microservice placement and migration schemes consider on-demand strategies. In this work, we take a different route and propose a Reinforcement Learning (RL) based proactive mechanism using a Learning Automata (LA) for microservice placement and migration that on one hand, keeps track of user mobility and resorts to migration when necessary, while on the other hand, keeps track of server residual capacities so that no server is overloaded. We use the San Francisco Taxi dataset to validate our approach. Experimental results show the effectiveness of our approach in comparison to other methods.

Multi-Access Edge Computing (MEC) is increasingly growing in prominence as the de facto enabler for ultra-low latency access to services. MEC averts the high network latencies often encountered in accessing cloud services by deploying application instances on edge servers situated near Internet-of-Things (IoT) device users. Workloads generated by IoT devices can then either be executed locally on the devices or offloaded to the MEC servers. A key cornerstone of the MEC environment is a service placement policy that determines the deployment of services on MEC servers. A service placement policy plays a critical role towards determining the trade-offs involved between latency experienced by users as a function of the resource contention and the resulting energy consumption. In this context, we propose a static-dynamic service placement policy for MEC. The static policy is geared towards placement of services in a prioritised order by leveraging Probabilistic Model Checking, a Formal Methods technique, to ensure probabilistic guarantees on the trade-offs between latencies and energy consumption of edge sites. The dynamic policy alters the static service allocation to cater to runtime variability in latency requirements. We present experimental results on a real-world service usage dataset to show the benefits of our approach over conventional approaches.

Non clear cell renal cell carcinoma (nccRCC) refers to a rare diverse heterogeneous group of tumors; usually treated with immunotherapy (IO) and or tyrosine kinase inhibitors (TKI). Prospective large-scale data from Asia specific countries is limited. We aimed to present the demographic profile and treatment outcome of nccRCC patients from single centre in India. This is a retrospective study on patients with metastatic nccRCC treated at Tata Medical Centre, Kolkata-West Bengal from 2012-2022. Demographic profiles, histologic subtypes, treatment details, response to therapy (by RECIST v1.1) and survival status were captured from electronic medical records of hospitals up till March 2023. Progression free survival (PFS) and Overall survival (OS) were estimated using Kaplan Meier method. A total of 89 patients were screened;24 excluded due to inadequate records. 65 patients were included in analysis, with a median age at diagnosis of 59 years (range 20-84). Histologic subtypes comprised 43% (n=28) papillary, 31%(n=20) clear cell with mixed histology, 3%(2) sarcomatoid & 23%(15) others including chromophobe, oncocytoma. The most common site of metastasis was lung 62% (n=40).15% presented with haematuria and 62%(n=40) underwent cytoreductive nephrectomy. Majority received pazopanib 46% (n=30) then chemotherapy 20% (n=13) including bevacizumab plus erlotinib, sunitinib 15%(10), cabozantinib 14%(9). Only 3(5%) received IO plus TKI combination. The best response was CR in 1.5%, PR 20%, SD 51% & PD 23% as per RECIST v1.1. Total 17 (26%) patients required dose reduction & interruption due to adverse effects & 33% received second-line therapy with nivolumab,axitinib & everolimus were commonly used. After a median follow up of 44 months; median PFS was 13 months (95%CI 7.2-18.9) & median OS was 17 months (95%CI 12.1-22.1). Regression analysis identified better outcome for PFS & OS with sarcomatoid subtype & those underwent prior nephrectomy. The overall response and survival was quite impressive in comparison with published data; despite limited number of cases treated with IO due to its cost.This study also highlighted better survival with sarcomatoid subtype and prior nephrectomy.

The advent of Multi-Access Edge Computing (MEC) has enabled service providers to mitigate high network latencies often encountered in accessing cloud services by deploying containerized application instances on edge servers situated near end users. MEC servers are, however, susceptible to various types of failures such as communication link failures, hardware failures and so on. A fault recovery strategy determines which MEC servers to utilize to re-deploy application containers in the event of a failure. In this work, we propose a two-fold fault recovery strategy characterized by application priority. We propose a Formal Methods driven local recovery strategy for high-priority applications. We use Stochastic Multi-Player Games as a Formal Model to characterize the interactions between the different components in an MEC environment. We use objectives specified in Probabilistic Alternating-Time Temporal Logic with a Probabilistic Model Checker to derive recovery strategies considering all possible execution scenarios of the model. For lower priority applications, we resort to a global recovery strategy by designing a greedy heuristic considering each server's failure probability. We use benchmark datasets to validate our approach. Experimental results show an average 14% reduction in latency with our approach in comparison with other state-of-the-art methods.

Sustainable computing is gaining priority for data centers due to need for mandatory compliance with carbon emission reporting regulations. Hence, accurately estimating the energy consumption of servers in data centers has become quintessential. Apart from estimating carbon emissions at the server level, quantifying carbon emissions at the virtualization layer is also crucial because it provides a more precise and detailed understanding of the possible hotspots and mismatched utilization which can help in taking remedial actions. In this paper, we take a first step towards modeling energy consumption at the Logical PARtitions (LPARs) level of data centers driven by IBM POWER9 Systems. We experimentally validate our approach on utilization metrics from the data center of the CIO office of IBM and compare it with the instrumented energy measurements wherein we demonstrate on average 90–95 % prediction accuracy for our model.

Colo rectal cancer is the third most common cancer in the world. Clinical data on localized colon cancer and treatment outcome is scarce in the literature. This is a real-world study of the clinical profile and treatment patterns and outcomes of patients with nonmetastatic colon cancer from India. Here we report our experience in this patient population.

A key cornerstone of Multi-Access Edge Computing (MEC) is an offloading policy utilized to determine whether to execute computation tasks on IoT devices or to offload the tasks to MEC servers for processing. In this work, we propose a Probabilistic Model Checking based offloading policy catering to device user preferences. We model the interactions between the various components of the MEC environment using a Turn-Based Stochastic Multi-Player Game (SMG). We present experiments on practical scenarios on data gathered from a test-bed setup with benchmark applications to show the benefits of an adaptive preferenceaware approach over conventional approaches in MEC offloading.

With the rapid proliferation of strategic alliances between service providers, enterprises cooperate towards service quality improvement and provide lower cost service bundles. This article presents a novel solution to the minimum cost service bundle selection problem for workflows in the presence of singleton subscription costs and service bundle offerings and compatibility requirements. Given a workflow specifying a set of tasks and a set of candidate services for each task, with a set of compatibility constraints between services, the selection problem has the objective of selecting the most suitable service offering(s) for each task. In this article, we analyze the selection problem in the presence of service bundle offerings. We present a novel multi-partite hyper-graph visualization of the selection problem and analyze its hardness. Additionally we present a novel combination of ILP and abstraction refinement as a potential solution, that is shown to expedite a naïve ILP based solution. We present experiments to substantiate this claim.

In recent times, Multi-Access Edge Computing (MEC) has emerged as a new paradigm allowing low-latency access to services deployed on edge nodes offering computation, storage and communication facilities. Vendors deploy their services on MEC servers to improve performance and mitigate network latencies often encountered in accessing cloud services. An allocation policy determines how to allocate service requests from users to MEC servers. A number of proposals for binding user service requests to nearby edge servers enroute have been proposed in literature. However, none of these proposals, to the best of our knowledge, provide quantitative guarantees on performance metrics. Indeed, the evolving environment, along with a large allocation configuration space makes proving performance guarantees for such allocation policies a challenging task. Further, the implications of MEC server failures on allocation policies have been relatively unexplored. To address such issues, we propose a trace driven approach to derive a formal model of allocation policies and perform quantitative verification to produce probabilistic guarantees on performance metrics. We use the San Francisco taxi dataset, the LDNS availability dataset and allocation policies from recent literature to validate our approach. Experimental results demonstrate how our model can be utilized to quantitatively compare performance metrics of service allocation policies in MEC systems.

Multi-Access Edge Computing (MEC) has emerged as a promising new paradigm allowing low latency access to services deployed on edge servers to avert network latencies often encountered in accessing cloud services. A key component of the MEC environment is an auto-scaling policy which is used to decide the overall management and scaling of container instances corresponding to individual services deployed on MEC servers to cater to traffic fluctuations. In this work, we propose a Safe Reinforcement Learning (RL)-based auto-scaling policy agent that can efficiently adapt to traffic variations to ensure adherence to service specific latency requirements. We model the MEC environment using a Markov Decision Process (MDP). We demonstrate how latency requirements can be formally expressed in Linear Temporal Logic (LTL). The LTL specification acts as a guide to the policy agent to automatically learn auto-scaling decisions that maximize the probability of satisfying the LTL formula. We introduce a quantitative reward mechanism based on the LTL formula to tailor service specific latency requirements. We prove that our reward mechanism ensures convergence of standard Safe-RL approaches. We present experimental results in practical scenarios on a test-bed setup with real-world benchmark applications to show the effectiveness of our approach in comparison to other state-of-the-art methods in literature. Furthermore, we perform extensive simulated experiments to demonstrate the effectiveness of our approach in large scale scenarios.

In this paper, we present a load variation aware adaptive stochastic method for user service request allocation and service placement in Multi-Access Edge Computing (MEC) . Simulation based experimental results on the benchmark EUA dataset show that our approach can better handle workload fluctuations as compared to state of the art.

Multi-Access Edge Computing (MEC) is a promising new paradigm enabling low-latency access to services deployed on edge servers. This helps to avert network latencies often encountered in accessing cloud services. The cornerstone of a MEC environment is a resource allocation policy used to partition and allocate computational resources such as bandwidth, memory available on the edge server to user service invocations availing such services. In this work, we propose a generic data-driven framework to model and analyze such MEC resource allocation policies. We model a MEC system as a Turn-Based Stochastic Multi-Player Game and use Probabilistic Model Checking to derive quantitative guarantees on resource allocation policies against requirements expressed in Probabilistic Alternating-Time Temporal Logic with Rewards. We present results on state-of-the-art MEC resource allocation policies to demonstrate the effectiveness of our framework.

In recent times, Mobile Edge Computing (MEC) has emerged as a new paradigm allowing low-latency access to services deployed on edge nodes offering computation, storage and communication facilities. Vendors deploy their services on MEC servers to improve performance and mitigate network latencies often encountered in accessing cloud services. An allocation policy determines how to allocate service requests from mobile users to MEC servers. A number of proposals for binding user service requests to nearby edge servers enroute have been proposed in literature. However, none of these proposals, to the best of our knowledge, provide quantitative performance guarantees on the quality of service metrics. Indeed, the evolving environment, along with a large allocation configuration space makes proving performance guarantees for such allocation policies a challenging task. To address such issues, we propose a trace driven approach to derive a formal model of allocation policies and perform quantitative verification to produce probabilistic guarantees on performance metrics. We use benchmark real world MEC server and user datasets and a mobility aware allocation and migration policy from recent literature to validate our model. Experimental results show our model's effectiveness in quantitatively reasoning about service allocation performance metrics in MEC systems.

Mobile Edge Computing (MEC) policies that bind user service requests to edge servers, seldom take into account user preferences of Quality-of-Service (QoS) and the resulting Quality-of-Experience (QoE). In this paper, we design a novel user-centric optimal allocation policy considering the QoS preferences of users, with an attempt to maximize the overall QoE. Additionally, we propose a real-time mobility aware user-centric heuristic algorithm to solve the allocation problem by accommodating the time varying QoS demands of users. Experimental results on real data sets demonstrate the efficiency of our allocation scheme and a comparison with state-of-art approaches in MEC literature.

In recent times, Mobile Edge Computing (MEC) has emerged as a new paradigm allowing low-latency access to services deployed on edge nodes offering computation, storage and communication facilities. Vendors deploy their services on MEC servers to improve performance and mitigate network latencies often encountered in accessing cloud services. A service placement policy determines which services are deployed on which MEC servers. A number of mechanisms exist in literature to determine the optimal placement of services considering different performance metrics. However, for applications designed as microservice workflow architectures, service placement schemes need to be re-examined through a different lens owing to the inherent interdependencies which exist between microservices. Indeed, the dynamic environment, with stochastic user movement and service invocations, along with a large placement configuration space makes microservice placement in MEC a challenging task. Additionally, owing to user mobility, a placement scheme may need to be recalibrated, triggering service migrations to maintain the advantages offered by MEC. Existing microservice placement and migration schemes consider on-demand strategies. In this work, we take a different route and propose a Reinforcement Learning based proactive mechanism for microservice placement and migration. We use the San Francisco Taxi dataset to validate our approach. Experimental results show the effectiveness of our approach in comparison to other state-of-the-art methods.

In this paper, we address the web service selection problem for linear workflows. Given a linear workflow specifying a set of ordered tasks and a set of candidate services providing different features for each task, the selection problem deals with the objective of selecting the most eligible service for each task, given the ordering specified. A number of approaches to solving the selection problem have been proposed in literature. With web services growing at an incredible pace, service selection at the Internet scale has resurfaced as a problem of recent research interest. In this work, we present our approach to the selection problem using an abstraction refinement technique to address the scalability limitations of contemporary approaches. Experiments on web service benchmarks show that our approach can add substantial performance benefits in terms of space when compared to an approach without our optimization.

In Infrastructure-As-A-Service Computational Cloud, a set of resources is leased out depending upon the requirements. A resource provisioning algorithm carries out this task by considering some overall objectives of the system under consideration. The algorithm takes the specifications of the Virtual Machines required and the characteristics of the Physical Machines present in the Data Centers as input and produces an assignment of the Virtual Machines to the Physical Machines as output while considering the objective function. We design a Genetic Algorithm-based resource provisioning strategy which attempts to distribute the requests uniformly among the available Physical Machines so as to keep the load on the machines as uniform as possible.