210 research outputs found
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
Edge and Central Cloud Computing: A Perfect Pairing for High Energy Efficiency and Low-latency
In this paper, we study the coexistence and synergy between edge and central
cloud computing in a heterogeneous cellular network (HetNet), which contains a
multi-antenna macro base station (MBS), multiple multi-antenna small base
stations (SBSs) and multiple single-antenna user equipment (UEs). The SBSs are
empowered by edge clouds offering limited computing services for UEs, whereas
the MBS provides high-performance central cloud computing services to UEs via a
restricted multiple-input multiple-output (MIMO) backhaul to their associated
SBSs. With processing latency constraints at the central and edge networks, we
aim to minimize the system energy consumption used for task offloading and
computation. The problem is formulated by jointly optimizing the cloud
selection, the UEs' transmit powers, the SBSs' receive beamformers, and the
SBSs' transmit covariance matrices, which is {a mixed-integer and non-convex
optimization problem}. Based on methods such as decomposition approach and
successive pseudoconvex approach, a tractable solution is proposed via an
iterative algorithm. The simulation results show that our proposed solution can
achieve great performance gain over conventional schemes using edge or central
cloud alone. Also, with large-scale antennas at the MBS, the massive MIMO
backhaul can significantly reduce the complexity of the proposed algorithm and
obtain even better performance.Comment: Accepted in IEEE Transactions on Wireless Communication
Mobile, collaborative augmented reality using cloudlets
The evolution in mobile applications to support advanced interactivity and demanding multimedia features is still ongoing. Novel application concepts (e.g. mobile Augmented Reality (AR)) are however hindered by the inherently limited resources available on mobile platforms (not withstanding the dramatic performance increases of mobile hardware). Offloading resource intensive application components to the cloud, also known as "cyber foraging", has proven to be a valuable solution in a variety of scenarios. However, also for collaborative scenarios, in which data together with its processing are shared between multiple users, this offloading concept is highly promising. In this paper, we investigate the challenges posed by offloading collaborative mobile applications. We present a middleware platform capable of autonomously deploying software components to minimize average CPU load, while guaranteeing smooth collaboration. As a use case, we present and evaluate a collaborative AR application, offering interaction between users, the physical environment as well as with the virtual objects superimposed on this physical environment
Delay-aware power optimization model for mobile edge computing systems
Reducing the total power consumption and network
delay are among the most interesting issues facing
large-scale Mobile Cloud Computing (MCC) systems and
their ability to satisfy the Service Level Agreement (SLA).
Such systems utilize cloud computing infrastructure to support
offloading some of user’s computationally heavy tasks
to the cloud’s datacenters. However, the delay incurred
by such offloading process lead the use of servers (called
cloudlets) placed in the physical proximity of the users, creating
what is known as Mobile Edge Computing (MEC).
The cloudlet-based infrastructure has its challenges such
as the limited capabilities of the cloudlet system (in terms
of the ability to serve different request types from users
in vast geographical regions). To cover the users demand
for different types of services and in vast geographical
regions, cloudlets cooperate among each other by passing
user requests from one cloudlet to another. This cooperation
affects both power consumption and delay. In this work,
we present a mixed integer linear programming (MILP
A Review on Computational Intelligence Techniques in Cloud and Edge Computing
Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users’ requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This article provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions
Mobile cloud computing and network function virtualization for 5g systems
The recent growth of the number of smart mobile devices and the emergence of complex multimedia mobile applications have brought new challenges to the design of wireless mobile networks. The envisioned Fifth-Generation (5G) systems are equipped with different technical solutions that can accommodate the increasing demands for high date rate, latency-limited, energy-efficient and reliable mobile communication networks.
Mobile Cloud Computing (MCC) is a key technology in 5G systems that enables the offloading of computationally heavy applications, such as for augmented or virtual reality, object recognition, or gaming from mobile devices to cloudlet or cloud servers, which are connected to wireless access points, either directly or through finite-capacity backhaul links. Given the battery-limited nature of mobile devices, mobile cloud computing is deemed to be an important enabler for the provision of such advanced applications. However, computational tasks offloading, and due to the variability of the communication network through which the cloud or cloudlet is accessed, may incur unpredictable energy expenditure or intolerable delay for the communications between mobile devices and the cloud or cloudlet servers. Therefore, the design of a mobile cloud computing system is investigated by jointly optimizing the allocation of radio, computational resources and backhaul resources in both uplink and downlink directions. Moreover, the users selected for cloud offloading need to have an energy consumption that is smaller than the amount required for local computing, which is achieved by means of user scheduling.
Motivated by the application-centric drift of 5G systems and the advances in smart devices manufacturing technologies, new brand of mobile applications are developed that are immersive, ubiquitous and highly-collaborative in nature. For example, Augmented Reality (AR) mobile applications have inherent collaborative properties in terms of data collection in the uplink, computing at the cloud, and data delivery in the downlink. Therefore, the optimization of the shared computing and communication resources in MCC not only benefit from the joint allocation of both resources, but also can be more efficiently enhanced by sharing the offloaded data and computations among multiple users. As a result, a resource allocation approach whereby transmitted, received and processed data are shared partially among the users leads to more efficient utilization of the communication and computational resources.
As a suggested architecture in 5G systems, MCC decouples the computing functionality from the platform location through the use of software virtualization to allow flexible provisioning of the provided services. Another virtualization-based technology in 5G systems is Network Function Virtualization (NFV) which prescribes the instantiation of network functions on general-purpose network devices, such as servers and switches. While yielding a more flexible and cost-effective network architecture, NFV is potentially limited by the fact that commercial off-the-shelf hardware is less reliable than the dedicated network elements used in conventional cellular deployments. The typical solution for this problem is to duplicate network functions across geographically distributed hardware in order to ensure diversity. For that reason, the development of fault-tolerant virtualization strategies for MCC and NFV is necessary to ensure reliability of the provided services
Optimizing task allocation for edge compute micro-clusters
There are over 30 billion devices at the network edge. This is largely driven by the unprecedented growth of the Internet-of-Things (IoT) and 5G technologies. These devices are being used in various applications and technologies, including but not limited to smart city systems, innovative agriculture management systems, and intelligent home systems. Deployment issues like networking and privacy problems dictate that computing should occur close to the data source at or near the network edge.
Edge and fog computing are recent decentralised computing paradigms proposed to augment cloud services by extending computing and storage capabilities to the network’s edge to enable executing computational workloads locally. The benefits can help to solve issues such as reducing the strain on networking backhaul, improving network latency and enhancing application responsiveness. Many edge and fog computing deployment solutions and infrastructures are being employed to deliver cloud resources and services at the edge of the network — for example, cloudless and mobile edge computing.
This thesis focuses on edge micro-cluster platforms for edge computing. Edge computing micro-cluster platforms are small, compact, and decentralised groups of interconnected computing resources located close to the edge of a network. These micro-clusters can typically comprise a variety of heterogeneous but resource-constrained computing resources, such as small compute nodes like Single Board Computers (SBCs), storage devices, and networking equipment deployed in local area networks such as smart home management. The goal of edge computing micro-clusters is to bring computation and data storage closer to IoT devices and sensors to improve the performance and reliability of distributed systems. Resource management and workload allocation represent a substantial challenge for such resource-limited and heterogeneous micro-clusters because of diversity in system architecture. Therefore, task allocation and workload management are complex problems in such micro-clusters.
This thesis investigates the feasibility of edge micro-cluster platforms for edge computation. Specifically, the thesis examines the performance of micro-clusters to execute IoT applications. Furthermore, the thesis involves the evaluation of various optimisation techniques for task allocation and workload management in edge compute micro-cluster platforms. This thesis involves the application of various optimisation techniques, including simple heuristics-based optimisations, mathematical-based optimisation and metaheuristic optimisation techniques, to optimise task allocation problems in reconfigurable edge computing micro-clusters.
The implementation and performance evaluations take place in a configured edge realistic environment using a constructed micro-cluster system comprised of a group of heterogeneous computing nodes and utilising a set of edge-relevant applications benchmark. The research overall characterises and demonstrates a feasible use case for micro-cluster platforms for edge computing environments and provides insight into the performance of various task allocation optimisation techniques for such micro-cluster systems
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