Paola Gabriela Vinueza Naranjo

Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study

Fog computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms that, to date, have been considered standing-alone. However, because of their complementary features, we expect that their integration can foster a number of computing and network-intensive pervasive applications under the incoming realm of the future Internet. Motivated by this consideration, the goal of this position paper is fivefold. First, we review the technological attributes and platforms proposed in the current literature for the standing-alone FC and IoE paradigms. Second, by leveraging some use cases as illustrative examples, we point out that the integration of the FC and IoE paradigms may give rise to opportunities for new applications in the realms of the IoE, Smart City, Industry 4.0, and Big Data Streaming, while introducing new open issues. Third, we propose a novel technological paradigm, the Fog of Everything (FoE) paradigm, that integrates FC and IoE and then we detail the main building blocks and services of the corresponding technological platform and protocol stack. Fourth, as a proof-of-concept, we present the simulated energy-delay performance of a small-scale FoE prototype, namely, the V-FoE prototype. Afterward, we compare the obtained performance with the corresponding one of a benchmark technological platform, e.g., the V-D2D one. It exploits only device-to-device links to establish inter-thing “ad hoc” communication. Last, we point out the position of the proposed FoE paradigm over a spectrum of seemingly related recent research projects.

FLAPS: bandwidth and delay-efficient distributed data searching in Fog-supported P2P content delivery networks

Due to the growing interest for multimedia contents by mobile users, designing bandwidth and delay-efficient distributed algorithms for data searching over wireless (possibly, mobile) “ad hoc” Peer-to-Peer (P2P) content Delivery Networks (CDNs) is a topic of current interest. This is mainly due to the limited computing-plus-communication resources featuring state-of-the-art wireless P2P CDNs. In principle, an effective means to cope with this limitation is to empower traditional P2P CDNs by distributed Fog nodes. Motivated by this consideration, the goal of this paper is twofold. First, we propose and describe the main building blocks of a hybrid (e.g., mixed infrastructure and “ad hoc”) Fog-supported P2P architecture for wireless content delivery, namely, the Fog-Caching P2P architecture. It exploits the topological (possibly, time varying) information locally available at the serving Fog nodes, in order to speed up the data searching operations performed by the served peers. Second, we propose a bandwidth and delay-efficient, distributed and adaptive probabilistic search algorithm, that relies on the learning automata paradigm, e.g., the Fog-supported Learning Automata Adaptive Probabilistic Search (FLAPS) algorithm. The main feature of the FLAPS algorithm is the exploitation of the local topology information provided by the serving Fog nodes and the current status of the collaborating peers, in order to run a suitably distributed reinforcement algorithm for the adaptive discovery of peer-to-peer and peer-to-fog minimum-hop routes. The performance of the proposed FLAPS algorithm is numerically evaluated in terms of Success Rate, Hit-per-Query, Message-per-Query, Response Delay and Message Duplication Factor over a number of randomly generated benchmark CDN topologies. Furthermore, in order to corroborate the actual effectiveness of the FLAPS algorithm, extensive performance comparisons are carried out with some state-of-the-art searching algorithms, namely the Adaptive Probabilistic Search, Improved Adaptive Probabilistic Search and the Random Walk algorithms.

Q*: Energy and delay-efficient dynamic queue management in TCP/IP virtualized data centers

The emerging utilization of Software-as-a-Service (SaaS) Fog computing centers as an Internet virtual computing commodity is raising concerns over the energy consumptions of networked data centers for the support of delay-sensitive applications. In addition to the energy consumed by the servers, the energy wasted by the network devices that support TCP/IP reliable inter-Virtual Machines (VMs) connections is becoming a significant challenge. In this paper, we propose and develop a framework for the joint characterization and optimization of TCP/IP SaaS Fog data centers that utilize a bank of queues for increasing the fraction of the admitted workload. Our goal is two-fold: (i) we maximize the average workload admitted by the data center; and, (ii) we minimize the resulting networking-plus-computing average energy consumption. For this purpose, we exploit the Lyapunov stochastic optimization approach, in order to design and analyze an optimal (yet practical) online joint resource management framework, which dynamically performs: (i) admission control; (ii) dispatching of the admitted workload; (iii) flow control of the inter-VM TCP/IP connections; (iv) queue control; (v) up/down scaling of the processing frequencies of the instantiated VMs; and, (vi) adaptive joint consolidation of both physical servers and TCP/IP connections. The salient features of the resulting scheduler (e.g., the Q* scheduler) are that: (i) it admits distributed and scalable implementation; (ii) it provides deterministic bounds on the instantaneous queue backlogs; (iii) it avoids queue overflow phenomena; and, (iv) it effectively tracks the (possibly unpredictable) time-fluctuations of the input workload, in order to perform joint resource consolidation without requiring any a priori information and/or forecast of the input workload. Actual energy and delay performances of the proposed scheduler are numerically evaluated and compared against the corresponding ones of some competing and state-of-the-art schedulers, under: (i) Fast - Giga - 10Giga Ethernet switching technologies; (ii) various settings of the reconfiguration-consolidation costs; and, (iii) synthetic, as well as real-world workloads. The experimental results support the conclusion that the proposed scheduler can achieve over 30% energy savings.

Design and energy-efficient resource management of virtualized networked Fog architectures for the real-time support of IoT applications

With the incoming 5G access networks, it is forecasted that Fog computing (FC) and Internet of Things (IoT) will converge onto the Fog-of-IoT paradigm. Since the FC paradigm spreads, by design, networking and computing resources over the wireless access network, it would enable the support of computing-intensive and delay-sensitive streaming applications under the energy-limited wireless IoT realm. Motivated by this consideration, the goal of this paper is threefold. First, it provides a motivating study the main “killer” application areas envisioned for the considered Fog-of-IoT paradigm. Second, it presents the design of a CoNtainer-based virtualized networked computing architecture. The proposed architecture operates at the Middleware layer and exploits the native capability of the Container Engines, so as to allow the dynamic real-time scaling of the available computing-plus-networking virtualized resources. Third, the paper presents a low-complexity penalty-aware bin packing-type heuristic for the dynamic management of the resulting virtualized computing-plus-networking resources. The proposed heuristic pursues the joint minimization of the networking-plus-computing energy by adaptively scaling up/down the processing speeds of the virtual processors and transport throughputs of the instantiated TCP/IP virtual connections, while guaranteeing hard (i.e., deterministic) upper bounds on the per-task computing-plus-networking delays. Finally, the actual energy performance-versus-implementation complexity trade-off of the proposed resource manager is numerically tested under both wireless static and mobile Fog-of-IoT scenarios and comparisons against the corresponding performances of some state-of-the-art benchmark resource managers and device-to-device edge computing platforms are also carried out.

A new Stable Election-based routing algorithm to preserve aliveness and energy in fog-supported wireless sensor networks

One of the current key challenges in wireless sensor networks is the development of routing protocols that provide stable cluster-head election, while prolonging network lifetime by saving energy. In this contribution, a new Stable Election Protocol (SEP), named New-SEP (N-SEP), is presented to prolong the stable period of Fog-supported sensor networks by maintaining balanced energy consumption. N-SEP takes into account some features of sensor nodes (e.g., distance from base station, network heterogeneity ratio, residual/consumed energy, distance between cluster heads (CHs)) in order to elect the best CHs. For this purpose, it exploits heterogeneous energy thresholds, in order to select CHs and prolong the time interval of the system. Simulation results support the capability of the proposed algorithm to maximize the network lifetime and preserve more energy as compared to the results obtained by using current heuristics, such as, Low Energy Adaptive Clustering Hierarchy (LEACH) and SEP protocols. Additionally, we found that N-SEP outperforms LEACH and SEP in prolonging the stability period of the network by 50% and 25%, respectively.

A Novel Distributed Fog-Based Networked Architecture to Preserve Energy in Fog Data Centers

The distinguishing feature of the Fog Computing (FC) paradigm is that FC spreads communication and computing resources over the wireless access network, so as to provide resource augmentation to resource and energy-limited wireless (possibly mobile) devices. Since FC would lead to substantial reductions in energy consumption and access latency, it will play a key role in the realization of the Fog of Everything (FoE) paradigm. The core challenge of the resulting FoE paradigm is tomaterialize the seamless convergence of three distinct disciplines, namely, broadband mobile communication, cloud computing, and Internet of Everything (IoE). In this paper, we present a new IoE architecture for FC in order to implement the resulting FoE technological platform. Then, we elaborate the related Quality of Service (QoS) requirements to be satisfied by the underlying FoE technological platform. Furthermore, in order to corroborate the conclusion that advancements in the envisioned architecture description, we present: (i) the proposed energy-aware algorithm adopt Fog data center; and, (ii) the obtained numerical performance, for a real-world case study that shows that our approach saves energy consumption impressively in theFog data Center compared with the existing methods and could be of practical interest in the incoming Fog of Everything (FoE) realm.

Energy performance of heuristics and meta-heuristics for real-time joint resource scaling and consolidation in virtualized networked data centers

In this paper, we explore on a comparative basis the performance suitability of meta-heuristic, sometime denoted as random search algorithms, and greedy-type heuristics for the energy-saving joint dynamic scaling and consolidation of the network-plus-computing resources hosted by networked virtualized data centers when the target is the support of real-time streaming-type applications. For this purpose, the energy and delay performances of Tabu Search (TS), Simulated Annealing (SA) and Evolutionary Strategy (ES) meta-heuristics are tested and compared with the corresponding ones of Best-Fit Decreasing-type heuristics, in order to give insight on the resulting performance-versus-implementation complexity trade-offs. In principle, the considered meta-heuristics and heuristics are general formal approaches that can be applied to large classes of (typically, non-convex and mixed integer) optimization problems. However, specially for the meta-heuristics, a main challenge is to design them to properly address the real-time joint computing-plus-networking resource consolidation and scaling optimization problem. To this purpose, the aim of this paper is: (i) introduce a novel Virtual Machine Allocation (VMA) scheme that aims at choosing a suitable set of possible Virtual Machine placements among the (possibly, non-homogeneous) set of available servers; (ii) propose a new class of random search algorithms (RSAs) denoted as consolidation meta-heuristic, considering the VMA problem in RSAs. In particular, the design of novel variants of meta-heuristics, namely TS-RSC, SA-RSC and ES-RSC, is particularized to the resource scaling and consolidation (RSC) problem; (iii) compare the results of the obtained new RSAs class against some state-of-the-art heuristic approaches. A set of experimental results, both simulated and real-world ones, support the effectiveness of the proposed approaches against the traditional ones.

Memory and memoryless optimal time-window controllers for secondary users in vehicular networks

In this paper, a primary-secondary resource-management controller on vehicular networks is designed and tested. We formulate the resource-management problem as a constrained stochastic network utility maximization problem and derive the optimal resource management controller, which dynamically allocates the access time-windows to the secondary-users. We provide the optimal steady-state controllers under hard and soft primary-secondary collision constraints, showing as the hard controller does not present any optimality gap in the average utility respect to the soft one, while, on the contrary, it is able to make the outage-probability vanishing. Then, we present as a particular case the subset of memoryless controller, that are unable to exploit the system statistics, derive the throughput-gain of the general controllers with respect to the memoryless ones and discuss conditions of applicability and advantages of each subclass.