322 research outputs found
Joint Planning of Network Slicing and Mobile Edge Computing in 5G Networks
Multi-access Edge Computing (MEC) facilitates the deployment of critical
applications with stringent QoS requirements, latency in particular. Our paper
considers the problem of jointly planning the availability of computational
resources at the edge, the slicing of mobile network and edge computation
resources, and the routing of heterogeneous traffic types to the various
slices. These aspects are intertwined and must be addressed together to provide
the desired QoS to all mobile users and traffic types still keeping costs under
control. We formulate our problem as a mixed-integer nonlinear program (MINLP)
and we define a heuristic, named Neighbor Exploration and Sequential Fixing
(NESF), to facilitate the solution of the problem. The approach allows network
operators to fine tune the network operation cost and the total latency
experienced by users. We evaluate the performance of the proposed model and
heuristic against two natural greedy approaches. We show the impact of the
variation of all the considered parameters (viz., different types of traffic,
tolerable latency, network topology and bandwidth, computation and link
capacity) on the defined model. Numerical results demonstrate that NESF is very
effective, achieving near-optimal planning and resource allocation solutions in
a very short computing time even for large-scale network scenarios.Comment: Submitted to IEEE Transactions on Cloud Computin
Enhanced VIP Algorithms for Forwarding, Caching, and Congestion Control in Named Data Networks
Emerging Information-Centric Networking (ICN) architectures seek to optimally
utilize both bandwidth and storage for efficient content distribution over the
network. The Virtual Interest Packet (VIP) framework has been proposed to
enable joint design of forwarding, caching, and congestion control strategies
within the Named Data Networking (NDN) architecture. While the existing VIP
algorithms exhibit good performance, they are primarily focused on maximizing
network throughput and utility, and do not explicitly consider user delay. In
this paper, we develop a new class of enhanced algorithms for joint dynamic
forwarding, caching and congestion control within the VIP framework. These
enhanced VIP algorithms adaptively stabilize the network and maximize network
utility, while improving the delay performance by intelligently making use of
VIP information beyond one hop. Generalizing Lyapunov drift techniques, we
prove the throughput optimality and characterize the utility-delay tradeoff of
the enhanced VIP algorithms. Numerical experiments demonstrate the superior
performance of the resulting enhanced algorithms for handling Interest Packets
and Data Packets within the actual plane, in terms of low network delay and
high network utility.Comment: 11 pages, 4 figures, to appear in IEEE GLOBECOM 2016. arXiv admin
note: text overlap with arXiv:1310.556
Selfish Caching Games on Directed Graphs
Caching networks can reduce the routing costs of accessing contents by
caching contents closer to users. However, cache nodes may belong to different
entities and behave selfishly to maximize their own benefits, which often lead
to performance degradation for the overall network. While there has been
extensive literature on allocating contents to caches to maximize the social
welfare, the analysis of selfish caching behaviors remains largely unexplored.
In this paper, we model the selfish behaviors of cache nodes as selfish caching
games on arbitrary directed graphs with heterogeneous content popularity. We
study the existence of a pure strategy Nash equilibrium (PSNE) in selfish
caching games, and analyze its efficiency in terms of social welfare. We show
that a PSNE does not always exist in arbitrary-topology caching networks.
However, if the network does not have a mixed request loop, i.e., a directed
loop in which each edge is traversed by at least one content request, we show
that a PSNE always exists and can be found in polynomial time. Furthermore, we
can avoid mixed request loops by properly choosing request forwarding paths. We
then show that the efficiency of Nash equilibria, captured by the price of
anarchy (PoA), can be arbitrarily poor if we allow arbitrary content request
patterns, and adding extra cache nodes can make the PoA worse, i.e., cache
paradox happens. However, when cache nodes have homogeneous request patterns,
we show that the PoA is bounded even allowing arbitrary topologies. We further
analyze the selfish caching games for cache nodes with limited computational
capabilities, and show that an approximate PSNE exists with bounded PoA in
certain cases of interest. Simulation results show that increasing the cache
capacity in the network improves the efficiency of Nash equilibria, while
adding extra cache nodes can degrade the efficiency of Nash equilibria
Rate Allocation and Content Placement in Cache Networks
We introduce the problem of optimal congestion control in cache networks,
whereby \emph{both} rate allocations and content placements are optimized
\emph{jointly}. We formulate this as a maximization problem with non-convex
constraints, and propose solving this problem via (a) a Lagrangian barrier
algorithm and (b) a convex relaxation. We prove different optimality guarantees
for each of these two algorithms; our proofs exploit the fact that the
non-convex constraints of our problem involve DR-submodular functions
Terra: Scalable Cross-Layer GDA Optimizations
Geo-distributed analytics (GDA) frameworks transfer large datasets over the
wide-area network (WAN). Yet existing frameworks often ignore the WAN topology.
This disconnect between WAN-bound applications and the WAN itself results in
missed opportunities for cross-layer optimizations. In this paper, we present
Terra to bridge this gap. Instead of decoupled WAN routing and GDA transfer
scheduling, Terra applies scalable cross-layer optimizations to minimize WAN
transfer times for GDA jobs. We present a two-pronged approach: (i) a scalable
algorithm for joint routing and scheduling to make fast decisions; and (ii) a
scalable, overlay-based enforcement mechanism that avoids expensive switch rule
updates in the WAN. Together, they enable Terra to quickly react to WAN
uncertainties such as large bandwidth fluctuations and failures in an
application-aware manner as well. Integration with the FloodLight SDN
controller and Apache YARN, and evaluation on 4 workloads and 3 WAN topologies
show that Terra improves the average completion times of GDA jobs by
1.55x-3.43x. GDA jobs running with Terra meets 2.82x-4.29x more deadlines and
can quickly react to WAN-level events in an application-aware manner
How Much Cache is Needed to Achieve Linear Capacity Scaling in Backhaul-Limited Dense Wireless Networks?
Dense wireless networks are a promising solution to meet the huge capacity
demand in 5G wireless systems. However, there are two implementation issues,
namely the interference and backhaul issues. To resolve these issues, we
propose a novel network architecture called the backhaul-limited cached dense
wireless network (C-DWN), where a physical layer (PHY) caching scheme is
employed at the base stations (BSs) but only a fraction of the BSs have wired
payload backhauls. The PHY caching can replace the role of wired backhauls to
achieve both the cache-induced MIMO cooperation gain and cache-assisted
Multihopping gain. Two fundamental questions are addressed. Can we exploit the
PHY caching to achieve linear capacity scaling with limited payload backhauls?
If so, how much cache is needed? We show that the capacity of the
backhaul-limited C-DWN indeed scales linearly with the number of BSs if the BS
cache size is larger than a threshold that depends on the content popularity.
We also quantify the throughput gain due to cache-induced MIMO cooperation over
conventional caching schemes (which exploit purely the cached-assisted
multihopping). Interestingly, the minimum BS cache size needed to achieve a
significant cache-induced MIMO cooperation gain is the same as that needed to
achieve the linear capacity scaling.Comment: 14 pages, 8 figures, accepted by IEEE/ACM Transactions on Networkin
Study and analysis of mobility, security, and caching issues in CCN
Existing architecture of Internet is IP-centric, having capability to cope with the needs of the Internet users. Due to the recent advancements and emerging technologies, a need to have ubiquitous connectivity has become the primary focus. Increasing demands for location-independent content raised the requirement of a new architecture and hence it became a research challenge. Content Centric Networking (CCN) paradigm emerges as an alternative to IP-centric model and is based on name-based forwarding and in-network data caching. It is likely to address certain challenges that have not been solved by IP-based protocols in wireless networks. Three important factors that require significant research related to CCN are mobility, security, and caching. While a number of studies have been conducted on CCN and its proposed technologies, none of the studies target all three significant research directions in a single article, to the best of our knowledge. This paper is an attempt to discuss the three factors together within context of each other. In this paper, we discuss and analyze basics of CCN principles with distributed properties of caching, mobility, and secure access control. Different comparisons are made to examine the strengths and weaknesses of each aforementioned aspect in detail. The final discussion aims to identify the open research challenges and some future trends for CCN deployment on a large scale
Parallel Simulation of Very Large-Scale General Cache Networks
In this paper we propose a methodology for the study of general cache networks, which is intrinsically scalable and amenable to parallel execution. We contrast two techniques: one that slices the network, and another that slices the content catalog. In the former, each core simulates requests for the whole catalog on a subgraph of the original topology, whereas in the latter each core simulates requests for a portion of the original catalog on a replica of the whole network. Interestingly, we find out that when the number of cores increases (and so the split ratio of the network topology), the overhead of message passing required to keeping consistency among nodes actually offsets any benefit from the parallelization: this is strictly due to the correlation among neighboring caches, meaning that requests arriving at one cache allocated on one core may depend on the status of one or more caches allocated on different cores. Even more interestingly, we find out that the newly proposed catalog slicing, on the contrary, achieves an ideal speedup in the number of cores. Overall, our system, which we make available as open source software, enables performance assessment of large scale general cache networks, i.e., comprising hundreds of nodes, trillions contents, and complex routing and caching algorithms, in minutes of CPU time and with exiguous amounts of memory
Optimal and quasi-optimal energy-efficient storage sharing for opportunistic sensor networks
This paper investigates optimum distributed storage techniques for data preservation, and eventual dissemination, in opportunistic heterogeneous wireless sensor networks where data collection is intermittent and exhibits spatio-temporal randomness. The proposed techniques involve optimally sharing the sensor nodes' storage and properly handling the storage traffic such that the buffering capacity of the network approaches its total storage capacity with minimum energy. The paper develops an integer linear programming (ILP) model, analyses the emergence of storage traffic in the network, provides performance bounds, assesses performance sensitivities and develops quasi-optimal decentralized heuristics that can reasonably handle the problem in a practical implementation. These include the Closest Availability (CA) and Storage Gradient (SG) heuristics whose performance is shown to be within only 10% and 6% of the dynamic optimum allocation, respectively
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