Sokol Kosta

Bio: Sokol Kosta is an academic researcher from Aalborg University – Copenhagen. The author has contributed to research in topics: Cloud computing & Mobile cloud computing. The author has an hindex of 17, co-authored 68 publications receiving 2291 citations. Previous affiliations of Sokol Kosta include Hong Kong University of Science and Technology & Sapienza University of Rome.

ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading

Abstract: Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method-level computation offloading. Advancing on previous work, it focuses on the elasticity and scalability of the cloud and enhances the power of mobile cloud computing by parallelizing method execution using multiple virtual machine (VM) images. We implement ThinkAir and evaluate it with a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for a N-queens puzzle application and one order of magnitude for a face detection and a virus scan application. We then show that a parallelizable application can invoke multiple VMs to execute in the cloud in a seamless and on-demand manner such as to achieve greater reduction on execution time and energy consumption. We finally use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements.

To offload or not to offload? The bandwidth and energy costs of mobile cloud computing

Abstract: The cloud seems to be an excellent companion of mobile systems, to alleviate battery consumption on smartphones and to backup user's data on-the-fly. Indeed, many recent works focus on frameworks that enable mobile computation offloading to software clones of smartphones on the cloud and on designing cloud-based backup systems for the data stored in our devices. Both mobile computation offloading and data backup involve communication between the real devices and the cloud. This communication does certainly not come for free. It costs in terms of bandwidth (the traffic overhead to communicate with the cloud) and in terms of energy (computation and use of network interfaces on the device). In this work we study the fmobile software/data backupseasibility of both mobile computation offloading and mobile software/data backups in real-life scenarios. In our study we assume an architecture where each real device is associated to a software clone on the cloud. We consider two types of clones: The off-clone, whose purpose is to support computation offloading, and the back-clone, which comes to use when a restore of user's data and apps is needed. We give a precise evaluation of the feasibility and costs of both off-clones and back-clones in terms of bandwidth and energy consumption on the real device. We achieve this through measurements done on a real testbed of 11 Android smartphones and an equal number of software clones running on the Amazon EC2 public cloud. The smartphones have been used as the primary mobile by the participants for the whole experiment duration.

Small World in Motion (SWIM): Modeling Communities in Ad-Hoc Mobile Networking

Abstract: The complexity of social mobile networks, networks of devices carried by humans (e.g. sensors or PDAs) and communicating with short-range wireless technology, makes it hard protocol evaluation. A simple and efficient mobility model such as SWIM reflects correctly kernel properties of human movement and, at the same time, allows to evaluate accurately protocols in this context. In this paper we investigate the properties of SWIM, in particular how SWIM is able to generate social behavior among the nodes and how SWIM is able to model networks with a power-law exponential decay dichotomy of inter contact time and with complex sub-structures (communities) as the ones observed in the real data traces. We simulate three real scenarios and compare the synthetic data with real world data in terms of inter-contact, contact duration, number of contacts, and presence and structure of communities among nodes and find out a very good matching. By comparing the performance of BUBBLE, a community-based forwarding protocol for social mobile networks, on both real and synthetic data traces, we show that SWIM not only is able to extrapolate key properties of human mobility but also is very accurate in predicting performance of protocols based on social human sub-structures.

FlopCoin: A Cryptocurrency for Computation Offloading

Abstract: During the last years, researche'rs have proposed solutions to help smartphones improve execution time and reduce energy consumption by offloading heavy tasks to remote entities. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices, giving birth to the Device-to-Device offloading paradigm. None of these strategies, though, offers any mechanism that considers selfish users and, most importantly, that motivates and defrays the participating devices who spend their resources. In this paper, we address these problems and propose the design of a framework that integrates an incentive scheme and a reputation mechanism. Our proposal follows the principles of the Hidden Market Design approach, which allows users to specify the amount of resources they are willing to sacrifice when participating in the offloading system. The underlying algorithm, that users are not aware of, is based on a truthful auction strategy and a peer-to-peer reputation exchange scheme. Extensive simulations on real traces depict how our designed mechanism achieves higher offloading rate and produces less traffic compared to three benchmark algorithms. Finally, we show how collaborating devices get rewarded for their contribution, while selfish ones get sidelined by others.

Before and After GDPR: The Changes in Third Party Presence at Public and Private European Websites

Abstract: The commencement of EU's General Data Protection Regulation (GDPR) has led to massive compliance and consent activities on websites. But did the new regulation result in fewer third party server appearances? Based on an eight months longitudinal study from February to September 2018 of 1250 popular websites in Europe and US, we present a mapping of the subtle shifts in the third party topology before and after May 25, 2018. The 1250 websites cover 39 European countries from EU, EEA, and outside EU, belonging to categories that cover both public-oriented citizen services, as well as commercially-oriented sites. The developments in the numbers and types of third party vary for categories of websites and countries. Analyzing the number of third parties over time, even though we notice a decline in the number of third parties in websites belonging to certain categories, we are cautious about attributing these effects to the general assumption that GDPR would lead to less third party activity. We believe that it is quite difficult to draw conclusions on cause-effect relationships in such a complex environment with many impacting factors.

Edge Computing: Vision and Challenges

Abstract: The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.

Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

Abstract: Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Abstract: Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Abstract: Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices

Abstract: Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost , which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the Lyapunov optimization-based dynamic computation offloading algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the computation task request, wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to corroborate the theoretical analysis as well as validate the effectiveness of the proposed algorithm.