Research Report
Smart Contracts for resource planning and
management
Cristina-Maria Miulescu
February 2022
Abstract
Cloud computing is slowly but steadily infiltrating many facets of our
lives. Besides standard Web services like Web mail, searching, and online education, the Internet of Things (IoT) has billions of gadgets that
submit data to the cloud. Processing IoT applications in the cloud may
not be the most efficient approach in every IoT scenario, particularly for
time-sensitive applications. Fog and edge computing, which deal with
the problem of managing the high data bandwidth required by end devices, are a viable option. These paradigms demand that huge amounts
of created data be processed locally, rather than in the cloud. Resource
management, which often focuses around resource allocation, workload
balance, resource provisioning, task scheduling, and QoS to achieve performance improvements, is one of the factors for cloud-based IoT settings.
In this report, I will discuss several studies that have looked at incorporating Blockchain-based technology for Edge Computing in order to show
that it is a low-cost, low-overhead tool for resource management.
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Introduction
Cloud computing has grown in popularity as a platform for storing and processing data from Internet of Things (IoT) devices.
The Internet of Things (IoT) links common gadgets to one another and
to the rest of the Internet, allowing for more meaningful interactions between
objects and humans. Typically, the connecting procedure connects sensing, actuating, and control devices. Furthermore, these devices follow the appropriate
communication protocols that are compliant with industry standards. In many
efficient and various methods, IoT can achieve the goal of smart recognizing,
discovering, following, and controlling things.
However, Cloud computing has several constraints, such as the requirement
to transfer data from each individual sensor to a data center through a network,
process the data, and then send commands to actuators. This is a significant
constraint because (a) transmission adds a significant amount of delay; and
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(b) because sensors and actuators are frequently on the same physical device,
control information may be outdated as well.
Cloud computing may be aided by fog and edge computing in addressing
these restrictions. Cloud computing is not totally replaced by fog computing and
edge computing, hence they are not alternatives. The three technologies, on the
other hand, can work together to enhance latency, dependability, and reaction
times. Location awareness is also enabled by the fog layer’s geo-distributed
nature and the edge devices’ geo-distributed nature (see the next paragraphs).
One of the most important distinctions between fog and edge computing is the
location of intelligence and processing capacity.
Figure 1: Cloud vs Fog vs Edge Computing
Fog computing uses a network of nodes to connect the cloud to the end
devices where intelligence can be found. Base stations or access points are
represented by these smart nodes. Fog computing can analyse IoT data in close
proximity to the data sources by bringing insight away from the cloud. After
then, it can use cloud resources (only if necessary) in a more efficient manner
than individual devices. Fog computing, for example, can relocate intelligence
to a Local Area Network (LAN) position in the network architecture, allowing
data processing in a fog node or an IoT gateway to be supported.
Edge computing entails bringing an edge gateway’s intelligence, processing
capacity, and intercommunication capabilities directly to the devices. It usually
isn’t associated with any cloud-based services and instead focuses on the IoT
device side. Mobile services, for example, require ultra-low latency and real-time
access to a radio network. Edge computing is a method of bringing compute
and communication resources from the cloud to the edge of the network. This
is done to facilitate services by eliminating latency and so providing users with
quick message delivery.
Certain networking difficulties, such as availability, scalability, and interoperability, may be partially addressed when resources are consolidated within the
cloud. However, fog and edge computing can help solve some of the novel difficulties (such network bottlenecks and latency). How to quantify the trade-off
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between data distribution and services at the fog or cloud levels is a specific difficulty. Smart service placement is one method to solve this problem. This may
be accomplished more explicitly through data localization, which is achieved by
locating the required services closer to the data that they manage. Bittencourt
et al. define suitable candidates as applications that do not require a lot of
processing power and are capable of evaluating vast amounts of data.
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Challenges in Resource Allocation for Cloud,
Fog, and Edge Computing
In cloud, fog, and edge computing, the issues with IoT equipment include responding to resource requirements and load balancing. In this article, load balancing is discussed as one of the important tactics for achieving optimal resource
use and reducing or avoiding congestion. As a result, achieving load balancing
for processing nodes in a fog environment while also running an IoT application
is a unique problem. According to [2, the decided task was to decrease energy
usage by decreasing latency and managing workload. The restrictions discussed
in [5] are connected to cloud computing and cloud-based synchronization, which
is a basic cloud computing function. The majority of data is synchronized to the
cloud via IoT devices. This particular design has two hurdles in terms of security
and efficiency. The following factors of data integrity, privacy, and availability
are key to cloud storage security concerns. Data exposure, data inadequacy,
malicious user handling, improper use of cloud computing and its services, and
perhaps session theft during data access are among the documented security
dangers. In detention-sensitive applications, issues like connection cost and latency between the cloud system and edge layer devices are unacceptable.
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Smart Contracts on Blockchain
Decentralization, non-tampering, full process traceability, collaborative maintenance, openness, and transparency are all properties of blockchain, which is
effectively a distributed shared ledger and database. Currency distinguishes
blockchain 1.0, smart contracts distinguish blockchain 2.0, and decentralized
applications distinguish blockchain 3.0. Consensus allows all ledger nodes to
agree on the legitimacy of a record, which also serves as a safeguard against
manipulation. It allows non-trusting nodes in a decentralized network to validate the legitimacy of a blockchain without the involvement of a third party.
The distributed node (miner) can only add its packed blocks to the blockchain
after solving the computational difficulty of determining the qualifying nonce
value for the block header.
Smart contracts can automatically execute some predetermined rules and
terms based on this trustworthy and tamper-proof data. Blockchain has introduced two new functionalities to the digital world: ”value representation” and
”value transfer,” transforming the internet from a ”information internet” to a
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”value internet.” Token symbolizes blockchain value in the digital world, which
is mirrored in four quadrants split by two dimensions: the digital world/physical
world/information internet/value internet, and its on-chain via smart contracts
and off-chain via Oracle interface.
Figure 2: Smart Contracts
A smart contract is a computerized transaction mechanism that lives on the
blockchain and has its own address. It may perform basic value transfers as well
as define sophisticated regulations. It runs automatically and independently,
and its software is similar to that of a special server daemon. The blockchain
records ”states,” and smart contracts are the means through which states may
be changed. The trustworthy intelligent platform for blockchain is made up of
programming languages, tools (smart contracts IDE), standards (ERC20), and
an operating environment (EVM). Ethereum’s smart contracts, for example,
operate on the Turing-complete programming language EVM (Ethereum Virtual
Machine) (Solidity).
The logical relationship between complicated smart contracts is turned into
program logic flow, which has the following life cycle: Set up (own an account
book wallet, i.e., address); Freeze (persistence by authentication); Execution
(condition trigger, update status, submit to blockchain, consensus verification);
and Finish (condition trigger, update status, submit to blockchain, consensus
verification) (transaction and new status information are stored in blockchain).
This is taken a step further with a blockchain that supports smart contracts,
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which enables for multi-step procedures. Smart contracts are self-contained
agents whose actions are totally predictable. As a result, they may be trusted
to advance any on-chain logic that can be described as a function of on-chain
data inputs, as long as the data they need to handle is within their grasp.
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Related Work on Blockchain integration for
Edge Computing
In this section, I will discuss several relevant studies in which Blockchain-based
technologies for IoT, Cloud, and Edge Computing have been incorporated.
The Ankr project is a decentralized cloud solution in development with the
goal of providing Clients with the infrastructure to run applications at lower
costs than traditional cloud service providers, as well as Data Centers with
the infrastructure to generate new revenue streams from underutilized capacity.
This will be accomplished by guaranteeing high availability of services, ease of
integration, and secure communication. Containerisation, cluster orchestration,
and Trusted Execution Environments will be used to achieve this goal (TEE).
Traditionally, cloud computing has been created through constructing Virtual Machines, which are emulations that allow you to operate what appear to
be several computers on a single piece of hardware. Virtual Machines consume a
lot of system resources (CPU and RAM), take a long time to boot up, and need
complicated software development resource management. Containerisation, on
the other hand, just virtualizes a computer’s operating system, allowing distributed applications to function without requiring each program to start its
own virtual machine (VM). Containers start in seconds, and the orchestration
mechanism that handles the interconnections and interactions among workloads
on the cloud infrastructure makes software development resource management
easier and faster.
The Dfinity project is a decentralized cloud solution with the goal of creating
a global supercomputer with ”unlimited” capacity and processing power. It
established the notion of ”AI is law,” in which everything is subject to an
intermediary-free algorithmic governance system that combines crowd wisdom
and traditional AI technology to freeze misbehaving smart contracts that are
harmful to the platform’s users’ interests. This practically indicates that if the
algorithmic governance system approves a transaction, it may be amended and
turned back. This is in contrast to projects like Bitcoin or Ethereum, where the
rule ”The Code is law” demands That a user cannot revert back a transaction
after it has been completed.
Huh et al. studied the synchronization issue in IoT and suggested a sevenlayer blockchain platform–based IoT device management solution. A smart
contract was used to create the platform, which was developed on Ethereum.
The significance of a smart contract on the blockchain network is highlighted in
this study.
The study of Xiong et al. focuses on the establishment of a mobile chain
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employing edge computing as a supplement to prior applications in healthcare,
finance, and other fields. This research examines a prototype model in two
scenarios: one in which the relationship between mining rewards and optimal
edge service price is fixed, and another in which it is variable, and provides
results that these types of providers can use when defining an optimal resource
management policy.
SmartEdge is a new Ethereum-based edge computing smart contract. It
enables nodes to offload computation to edge computing devices owned by other
parties in exchange for payment in a verifiable way. The computing node is the
first party. The compute node is the computational resource that will be made
available to conduct tasks under the SmartEdge contract. The data node, often
known as the counterparty, is the second party.
Figure 3: SmartEdge participants
Sabir et al. want to show how Ethereum and a Smart Contract may be
used to protect mobile agents in the context of the Internet of Things. The
transactions on the Blockchain are used to identify malicious mobile agents
that could compromise IoT networks. The suggested architecture intends to
enable safe mobile agent migration in order to assure security and prevent IoT
applications from malicious agents. The suggested method is tested using the
example of a smart house with several applications. Outside of the smart home,
the methodology given in this research might be used to a broader range of IoT
systems.
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Conclusion
We’ve seen an explosion of data due to significant advancements in cloud computing technologies and IoT applications. Data centers create massive volumes
of data, which not only elicit a variety of intriguing data-driven services but
also consume a lot of energy every day.
I evaluated several resource management difficulties found in cloud/fog and
edge settings in my paper. Furthermore, numerous strategies for integrating
Blockchain technology into IoT applications using Smart Contracts have been
demonstrated. This study project might go in a number of different paths in the
future. One of the goals of future research is to find different resource allocation
/ reallocation approaches using multi-objective optimization methodologies.
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References
[1] Huh, Seyoung, Sangrae Cho, and Soohyung Kim. ”Managing IoT devices
using blockchain platform.” 2017 19th international conference on advanced
communication technology (ICACT). IEEE, 2017.
[2] Christidis, Konstantinos, and Michael Devetsikiotis. ”Blockchains and smart
contracts for the internet of things.” Ieee Access 4 (2016): 2292-2303.
[3] Zhou, Yiyun, et al. ”Improving iot services in smart-home using blockchain
smart contract.” 2018 IEEE International Conference on Internet of Things
(iThings) and IEEE Green Computing and Communications (GreenCom)
and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE
Smart Data (SmartData). IEEE, 2018.
[4] Mohanta, Bhabendu Kumar, Soumyashree S. Panda, and Debasish Jena.
”An overview of smart contract and use cases in blockchain technology.”
2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2018.
[5] Zhang, Yuanyu, et al. ”Smart contract-based access control for the internet
of things.” IEEE Internet of Things Journal 6.2 (2018): 1594-1605.
[6] Xu, Ronghua, Yu Chen, and Erik Blasch. ”Decentralized access control for
IoT based on blockchain and smart contract.” Modeling and Design of Secure
Internet of Things (2020): 505-528.
[7] Gulati, Ajay, et al. ”Cloud scale resource management: Challenges and techniques.” 3rd USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 11). 2011.
[8] Nzanywayingoma, Frederic, and Yang Yang. ”Efficient resource management
techniques in cloud computing environment: a review and discussion.” International Journal of Computers and Applications 41.3 (2019): 165-182.
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